This volume argues for a new image of science that understands both natural and social phenomena to be the product of mechanisms, casting the work of science as an effort to understand those mechanisms. Glennan offers an account of the nature of mechanisms and of the models used to represent them in physical, life, and social sciences.

The transition from chemistry to biology is an extremely complex issue because of the huge phenomenological differences between the two domains and because this transition has many different aspects and dimensions. In this paper, I will try to analyze how chemical systems have developed a cohesive, self-maintaining and functionally differentiated system that recruits its organization to stay far from equilibrium. This organization cannot exist but in an individualized form, and yet, it unfolds both a diachronic-historical and a synchronic collective dimension. (...) I will argue that, far from being a problem, these different dimensions of the phenomenon of life, appear as a consequence of the nature of this individualized organization. (shrink)

The concept of mechanism is analyzed in terms of entities and activities, organized such that they are productive of regular changes. Examples show how mechanisms work in neurobiology and molecular biology. Thinking in terms of mechanisms provides a new framework for addressing many traditional philosophical issues: causality, laws, explanation, reduction, and scientific change.

Complexity arises from interaction dynamics, but its forms are co-determined by the operative constraints within which the dynamics are expressed. The basic interaction dynamics underlying complex systems is mostly well understood. The formation and operation of constraints is often not, and oftener under appreciated. The attempt to reduce constraints to basic interaction fails in key cases. The overall aim of this paper is to highlight the key role played by constraints in shaping the field of complex systems. Following an introduction (...) to constraints, the paper develops the roles of constraints in specifying forms of complexity and illustrates the roles of constraints in formulating the fundamental challenges to understanding posed by complex systems. (shrink)

The emulation theory of representation is developed and explored as a framework that can revealingly synthesize a wide variety of representational functions of the brain. The framework is based on constructs from control theory (forward models) and signal processing (Kalman filters). The idea is that in addition to simply engaging with the body and environment, the brain constructs neural circuits that act as models of the body and environment. During overt sensorimotor engagement, these models are driven by efference copies in (...) parallel with the body and environment, in order to provide expectations of the sensory feedback, and to enhance and process sensory information. These models can also be run off-line in order to produce imagery, estimate outcomes of different actions, and evaluate and develop motor plans. The framework is initially developed within the context of motor control, where it has been shown that inner models running in parallel with the body can reduce the effects of feedback delay problems. The same mechanisms can account for motor imagery as the off-line driving of the emulator via efference copies. The framework is extended to account for visual imagery as the off-line driving of an emulator of the motor-visual loop. I also show how such systems can provide for amodal spatial imagery. Perception, including visual perception, results from such models being used to form expectations of, and to interpret, sensory input. I close by briefly outlining other cognitive functions that might also be synthesized within this framework, including reasoning, theory of mind phenomena, and language. Key Words: efference copies; emulation theory of representation; forward models; Kalman filters; motor control; motor imagery; perception; visual imagery. (shrink)

This paper outlines an original interactivist-constructivist approach to modelling intelligence and learning as a dynamical embodied form of adaptiveness and explores some applications of I-C to understanding the way cognitive learning is realized in the brain. Two key ideas for conceptualizing intelligence within this framework are developed. These are: intelligence is centrally concerned with the capacity for coherent, context-sensitive, self-directed management of interaction; and the primary model for cognitive learning is anticipative skill construction. Self-directedness is a capacity for integrative process (...) modulation which allows a system to "steer" itself through its world by anticipatively matching its own viability requirements to interaction with its environment. Because the adaptive interaction processes required of intelligent systems are too complex for effective action to be prespecified learning is an important component of intelligence. A model of self-directed anticipative learning is formulated based on interactive skill construction, and argued to constitute a central constructivist process involved in cognitive development. SDAL illuminates the capacity of intelligent learners to start with the vague, poorly defined problems typically posed in realistic learning situations and progressively refine them, transforming them into problems with sufficient structure to guide the construction of a solution. Finally, some of the implications of I-C for modelling of the neuronal basis of intelligence and learning are explored; in particular, Quartz and Sejnowski's recent neural constructivism paradigm, enriched by Montague and Sejnowski's dopaminergic model of anticipative-predictive neural learning, is assessed as a promising, but incomplete, contribution to this approach. The paper concludes with a fourfold reflection on the divergence in cognitive modelling philosophy between the I-C and the traditional computational information processing approaches. (shrink)

Explanations in the life sciences frequently involve presenting a model of the mechanism taken to be responsible for a given phenomenon. Such explanations depart in numerous ways from nomological explanations commonly presented in philosophy of science. This paper focuses on three sorts of differences. First, scientists who develop mechanistic explanations are not limited to linguistic representations and logical inference; they frequently employ diagrams to characterize mechanisms and simulations to reason about them. Thus, the epistemic resources for presenting mechanistic explanations are (...) considerably richer than those suggested by a nomological framework. Second, the fact that mechanisms involve organized systems of component parts and operations provides direction to both the discovery and testing of mechanistic explanations. Finally, models of mechanisms are developed for specific exemplars and are not represented in terms of universally quantified statements. Generalization involves investigating both the similarity of new exemplars to those already studied and the variations between them. (shrink)

What makes a living system a living system? What kind of biological phenomenon is the phenomenon of cognition? These two questions have been frequently considered, but, in this volume, the authors consider them as concrete biological questions. Their analysis is bold and provocative, for the authors have constructed a systematic theoretical biology which attempts to define living systems not as objects of observation and description, nor even as interacting systems, but as self-contained unities whose only reference is to themselves. The (...) consequence of their investigations and of their living systems as self-making, self-referring autonomous unities, is that they discovered that the two questions have a common answer: living systems are cognitive systems, and living as a process is a process of cognition. The result of their investigations is a completely new perspective of biological (human) phenomena. During the investigations, it was found that a complete linguistic description pertaining to the ‘organization of the living’ was lacking and, in fact, was hampering the reporting of results. Hence, the authors have coined the word ‘autopoiesis’ to replace the expression ‘circular organization’. Autopoiesis conveys, by itself, the central feature of the organization of the living, which is autonomy. (shrink)

An analysis of two heuristic strategies for the development of mechanistic models, illustrated with historical examples from the life sciences. In Discovering Complexity, William Bechtel and Robert Richardson examine two heuristics that guided the development of mechanistic models in the life sciences: decomposition and localization. Drawing on historical cases from disciplines including cell biology, cognitive neuroscience, and genetics, they identify a number of "choice points" that life scientists confront in developing mechanistic explanations and show how different choices result in divergent (...) explanatory models. Describing decomposition as the attempt to differentiate functional and structural components of a system and localization as the assignment of responsibility for specific functions to specific structures, Bechtel and Richardson examine the usefulness of these heuristics as well as their fallibility—the sometimes false assumption underlying them that nature is significantly decomposable and hierarchically organized. When Discovering Complexity was originally published in 1993, few philosophers of science perceived the centrality of seeking mechanisms to explain phenomena in biology, relying instead on the model of nomological explanation advanced by the logical positivists (a model Bechtel and Richardson found to be utterly inapplicable to the examples from the life sciences in their study). Since then, mechanism and mechanistic explanation have become widely discussed. In a substantive new introduction to this MIT Press edition of their book, Bechtel and Richardson examine both philosophical and scientific developments in research on mechanistic models since 1993. (shrink)

A fascinating exploration of the very essence of life itself sheds new light on the order and evolution in complex life systems and defines and explains autonomous agents and work within the contexts of thermodynamics and information theory, setting the stage for a dramatic technological revolution. 50,000 first printing.

Although now replaced by more modern theories, classical mechanics remains a core foundational element of physical theory. From its inception, the theory of dynamics has been riddled with conceptual issues and differing philosophical interpretations and throughout its long historical development, it has shown subtle conceptual refinement. The interpretive program for the theory has also shown deep evolutionary change over time. Lawrence Sklar discusses crucial issues in the central theory from which contemporary foundational theories are derived and shows how some core (...) issues have nevertheless remained deep puzzles despite the increasingly sophisticated understanding of the theory which has been acquired over time. His book will be of great interest to philosophers of science, philosophers in general and physicists concerned with foundational interpretive issues in their field. (shrink)

Positing representations and operations on them as a way of explaining behavior was one of the major innovations of the cognitive revolution. Neuroscience and biology more generally also employ representations in explaining how organisms function and coordinate their behavior with the world around them. In discussions of the nature of representation, theorists commonly differentiate between the vehicles of representation and their content—what they denote. Many contentious debates in cognitive science, such as those pitting neural network models against symbol processing accounts, (...) have focused on the types of vehicles proposed for mental representation and whether they have the appropriate structure to succeed in bearing their contents. Philosophers, in contrast, have focused their debates on content and the particular way in which vehicles might bear content—that is, the process of representing rather than the format of representations. I will offer a novel answer to the question of how it is that a representation has content by focusing on the architecture of representation. My proposal is that representations occur in a particular type of mechanism—one in which a control system regulates a plant—and that we can gain traction on cognitive systems of representation by considering how this works in physical systems more generally. (shrink)

In this paper we address the question of minimal cognition by investigating the origin of some crucial cognitive properties from the very basic organisation of biological systems. More specifically, we propose a theoretical model of how a system can distinguish between specific features of its interaction with the environment, which is a fundamental requirement for the emergence of minimal forms of cognition. We argue that the appearance of this capacity is grounded in the molecular domain, and originates from basic mechanisms (...) of biological regulation. In doing so, our aim is to provide a theoretical account that can also work as a possible conceptual bridge between Synthetic Biology and Artificial Intelligence. In fact, we argue, Synthetic Biology can contribute to the study of minimal cognition, by providing a privileged approach to the study of these mechanisms by means of artificial systems. (shrink)

Endocrinologists apply the idea of feedback loops to explain how hormones regulate certain bodily functions such as glucose metabolism. In particular, feedback loops focus on the maintenance of the plasma concentrations of glucose within a narrow range. Here, we put forward a different, organicist perspective on the endocrine regulation of glycaemia, by relying on the pivotal concept of closure of constraints. From this perspective, biological systems are understood as organized ones, which means that they are constituted of a set of (...) mutually dependent functional structures acting as constraints, whose maintenance depends on their reciprocal interactions. Closure refers specifically to the mutual dependence among functional constraints in an organism. We show that, when compared to feedback loops, organizational closure can generate much richer descriptions of the processes and constraints at play in the metabolism and regulation of glycaemia, by making explicit the different hierarchical orders involved. We expect that the proposed theoretical framework will open the way to the construction of original mathematical models, which would provide a better understanding of endocrine regulation from an organicist perspective. (shrink)

Dynamicism has provided cognitive science with important tools to understand some aspects of “how cognitive agents work” but the issue of “what makes something cognitive” has not been sufficiently addressed yet, and, we argue, the former will never be complete without the later. Behavioristic characterizations of cognitive properties are criticized in favor of an organizational approach focused on the internal dynamic relationships that constitute cognitive systems. A definition of cognition as adaptive-autonomy in the embodied and situated neurodynamic domain is provided: (...) the compensatory regulation of a web of stability dependencies between sensorimotor structures, is created and preserved during a historical/developmental process. We highlight the functional role of emotional embodiment: internal bioregulatory processes coupled to the formation and adaptive regulation of neurodynamic autonomy. Finally, we discuss a “minimally cognitive behavior program” in evolutionary simulation modelling suggesting that much is to be learned from a complementary “minimally cognitive organization program”. (shrink)

After half a century of cognitive revolution we remain far from agreement about what cognition is and what cognition does. It was once thought that these questions could wait until the data were in. Today there is a mountain of data, but no way of making sense of it. The time for tackling the fundamental issues has arrived. The biogenic approach to cognition is introduced not as a solution but as a means of approaching the issues. The traditional, and still (...) predominant, methodological stance in cognitive inquiry is what I call the anthropogenic approach: assume human cognition as the paradigm and work ‘down’ to a more general explanatory concept. The biogenic approach, on the other hand, starts with the facts of biology as the basis for theorizing and works ‘up’ to the human case by asking psychological questions as if they were biological questions. Biogenic explanations of cognition are currently clustered around two main frameworks for understanding biology: self-organizing complex systems and autopoiesis. The paper describes the frameworks and infers from them ten empirical principles—the biogenic ‘family traits’—that constitute constraints on biogenic theorizing. Because the anthropogenic approach to cognition is not constrained empirically to the same degree, I argue that the biogenic approach is superior for approaching a general theory of cognition as a natural phenomenon. (shrink)